This paper proposes a novel framework for autonomous unmanned aerial vehicle (UAV) navigation in complex environments, seamlessly integrating Theta* for global path planning with a simplified modulated velocity obstacle avoidance (MVOA) algorithm for local obstacle avoidance. Theta* generates optimal, smooth paths, while MVOA processes 2D LiDAR data as a single obstacle block to compute modulated velocities, enabling efficient avoidance of static and dynamic obstacles with minimal computational overhead. Compared to MVOA-only navigation, the integration of Theta* and MVOA produced shorter trajectories and faster mission completion with smoother velocity adjustments, demonstrating clear improvements in efficiency and stability. Simulation results show the framework maintains a 0.6 m safety distance and operates at 10 Hz, underscoring its robustness and reliability. The resulting control velocity is transmitted to an ArduPilot-based flight controller via MAVLink, ensuring precise, real-time execution. The current implementation focuses on 2D navigation in a planar environment as a foundation for future 3D expansion, with all results obtained through high-fidelity simulation. Building on these findings, the framework shows strong potential for real-time applications such as swarm UAV coordination, terrain surveying, and indoor navigation, offering a scalable solution for autonomous systems in dynamic settings.

Adaptive obstacle avoidance algorithm for wave gliders in dynamic marine environments based on improved DAPF with multi-model prediction

This paper introduces a spatiotemporally coupled coordination framework to address the limitations of conventional navigation strategies for UAV swarm operations, particularly with respect to dynamic obstacle avoidance and formation stability. The approach integrates an improved A* algorithm with dynamic potential field fusion, thereby ensuring global path optimality and local control feasibility. A sliding mode predictive control (SMPC) scheme is developed to guarantee exponential convergence of formation errors. Extensive experimental evaluations demonstrate an 81.5% reduction in path redundancy, a 100% success rate in dynamic obstacle avoidance, and an 86.5% enhancement in trajectory smoothness. These findings validate the superior effectiveness of the proposed method in complex and dynamic environments.

This paper presents a deep reinforcement learning (DRL)-based Multi-Agent Control for Formation and Obstacle Avoidance (MACFOA) algorithm to solve collaborative formation and obstacle avoidance decision-making for unmanned aerial vehicle (UAV) systems in dense obstacle environments. The algorithm primarily addresses the coupled strategy update challenges that emerge from simultaneous obstacle avoidance and formation control. A distributed control framework is employed to enable efficient formation and obstacle avoidance for UAV swarms in densely obstructed environments. To address the inefficiency and instability of strategy update problems due to sparse data samples in DRL formation control algorithms, an enhanced multi-step continuous experience replay mechanism is introduced. This mechanism stores and leverages experience data from multiple consecutive time steps, linking contextual information while fully accounting for temporal dependencies, ensuring continuous dynamic policy optimization throughout the training process. Comparative simulations were carried out to evaluate the performances of the proposed approach in terms of efficiency and flexibility. The results have shown that employing the MACFOA-MULT4 algorithm, which utilizes a four-step experience replay strategy, leads to optimal performance, significantly enhancing both training efficiency and stability. Compared to MACFOA, it reduces root mean square error (RMSE) by 38.56% and improves by 25.17% over the multi-agent recurrent deterministic policy gradient (MADRPG). In dynamic simulations on the AirSim platform, the algorithm demonstrated strong adaptability and stability, especially in high-obstacle-density environments. Its superior performance in control stability and task efficiency highlights the effectiveness and advantages of the proposed control strategy.

Aiming at the problem that obstacle avoidance flexibility and formation integrity are difficult to coexist in multi-robot formation motion, a path-deformation mapping mechanism is proposed, which deeply integrates artificial potential field and affine transformation, and drives formation adaptive adjustment in real time through path information. By using the non-uniform scaling characteristics of the affine transformation, the limitation of traditional conformal transformation is broken through, and the unity of flexibility and integrity is realized. The effectiveness of the algorithm is verified by experiments, which provide a practical solution for cooperative obstacle avoidance of multi-robot systems in complex environments. In order to verify the performance of the algorithm, a numerical simulation is carried out, and an experimental platform composed of seven omnidirectional mobile robots is built for physical verification. The simulation and experimental results show that the formation can complete the obstacle avoidance task in the complex static obstacle environment, and the average formation tracking error is maintained below 0.05 m. Compared with the traditional local obstacle avoidance or formation switching method, this algorithm significantly improves the fluency of the obstacle avoidance process and the integrity of the formation while ensuring a success rate of 100% obstacle avoidance.

This study explores the use of construction robots with collaborative path planning and coordination in complex building construction tasks. Current construction processes involving robots are often fragmented due to their single-task focus, with limited research focused on employing multiple construction robots to collaboratively perform tasks. To address such a challenge, this research proposes an improved A* algorithm for global path planning and obstacle avoidance, combined with the development of a BIM-based grid map of the construction site. The leader–follower method is utilized to guide the robot group in maintaining an optimal formation, ensuring smooth collaboration during construction. The methodology includes formalizing building construction site environments into BIM-based grid maps, path planning, and obstacle avoidance, which allows robot groups to autonomously navigate and complete specific tasks such as concrete, masonry, and decoration construction. The results of this study show that the proposed approach achieves significant reductions in pathlength and operational time of approximately 9% and 10%, respectively, while maintaining safety and efficiency compared with traditional manual methods. This research demonstrates the potential of collaborative construction robot groups to enhance productivity, reduce labor costs, and provide a scalable solution for the intelligent transformation of the construction industry; extends the classical A* algorithm by incorporating obstacle density into the heuristic function; and proposes a new node simplification strategy, contributing to the literature on robot motion planning in semi-structured environments.

Higher requirements have been placed on fault detection for continuously operating machines in modern factories. Manual inspection faces challenges related to timeliness, leading to the emergence of autonomous diagnostic robots. To overcome the safety limitations of existing diagnostic robots in factory environments, a hierarchical fuzzy logic-based navigation and obstacle avoidance algorithm is proposed in this study. The algorithm is constructed based on zero-order Takagi-Sugeno type fuzzy control, comprising subfunctions for navigation, static obstacle avoidance, and dynamic obstacle avoidance. Coordinated navigation and equipment protection are achieved by jointly considering the information of the fault point and surrounding equipment. The concept of a dynamic safety boundary is introduced, wherein the normalized breached level is used to replace the traditional distance-based input. In the inference process for dynamic obstacle avoidance, the relative speed direction is additionally considered. A Mamdani-type fuzzy inference system is employed to infer the necessity of obstacle avoidance and determine the priority target for avoidance, thereby enabling multi-objective planning. Simulation results demonstrate that the proposed algorithm can guide the diagnostic robot to within 30 cm of the fault point while ensuring collision avoidance with both equipment and obstacles, enhancing the completeness and safety of the fault point searching process.

We propose a speed-adaptive robot accurate path-tracking framework based on the soft actor–critic (SAC) and Stanley methods (STANLY_ASAC). First, the Lidar–Inertial Odometry Simultaneous Localization and Mapping (LIO-SLAM) method is used to map the environment and the LIO-localization framework is adopted to achieve real-time positioning and output the robot pose at 100 Hz. Next, the Rapidly exploring Random Tree (RRT) algorithm is employed for global path planning. On this basis, we integrate an improved A* algorithm for local obstacle avoidance and apply a gradient descent smoothing algorithm to generate a reference path that satisfies the robot’s kinematic constraints. Secondly, a network classification model based on U-Net is used to classify common road surfaces and generate classification results that significantly compensate for tracking accuracy errors caused by incorrect road surface coefficients. Next, we leverage the powerful learning capability of adaptive SAC (ASAC) to adaptively adjust the vehicle’s acceleration and lateral deviation gain according to the road and vehicle states. Vehicle acceleration is used to generate the real-time tracking speed, and the lateral deviation gain is used to calculate the front wheel angle via the Stanley tracking algorithm. Finally, we deploy the algorithm on a mobile robot and test its path-tracking performance in different scenarios. The results show that the proposed path-tracking algorithm can accurately follow the generated path.

ABSTRACTThis paper briefly introduces the blind spot detection and obstacle avoidance algorithm for autonomous electric vehicles that utilize laser radar and an onboard camera. Simulation experiments were conducted. During the experiments, the You Only Look Once version 5 (YOLOv5) model was tested for its ability to identify obstacles in road images captured by the onboard camera. Next, the effectiveness of the onboard camera combined with laser radar in accurately locating obstacles was assessed. Finally, the obstacle avoidance capability of the autonomous electric vehicle was tested. The results showed that the YOLOv5 model accurately located and identified obstacles in the image. The combination of the onboard camera and laser radar accurately determined the coordinates of obstacles. Moreover, the autonomous electric vehicle based on the onboard camera and laser radar successfully avoided obstacles and reached its destination without being affected by low‐light environments.

The paper aims to simulate UAVs in complex network environments to evaluate their performance in obstacle avoidance using stereo sensors. Since there are strict security regulations limiting real-world UAV flight testing, this research addresses the critical need for comprehensive simulation-based approaches.. The system integrates real-time obstacle detection and avoidance algorithms, enabling UAVs to navigate safely in environments with obstacles. Developed on the ROS framework with PX4 SITL and Gazebo, the system supports comprehensive end-to-end testing, from stereo image acquisition to UAV navigation using the 3DVFH* algorithm. It utilizes the MAVLink protocol for control and QGroundControl for monitoring. Simulation results confirm the system’s effectiveness, establishing a solid foundation for advanced autonomous UAV navigation in real-world applications.

To address issues such as inadequate robustness in dynamic obstacle avoidance, instability in formation morphology, severe resource conflicts in multi-task scenarios, and challenges in global path planning optimization for unmanned aerial vehicles (UAVs) operating in complex airspace environments, this paper examines the advantages and limitations of conventional UAV formation cooperative control theories. A multi-UAV cooperative control strategy is proposed, integrating adaptive game theory and deep reinforcement learning within a unified framework. By employing a three-layer information fusion architecture—comprising the physical layer, intent layer, and game-theoretic layer—the approach establishes models for multi-modal perception fusion, game-theoretic threat assessment, and dynamic aggregation-reconstruction. This optimizes obstacle avoidance algorithms, facilitates interaction and task coupling among formation members, and significantly improves the intelligence, resilience, and coordination of formation-wide cooperative control. The proposed solution effectively addresses the challenges associated with cooperative control of UAV formations in complex traffic environments.

Human-inspired dynamic obstacle and inter-collision avoidance algorithm for humanoid biped robots

This paper focuses on the application and verification of the Q-learning algorithm and the Sarsa algorithm in a robot obstacle avoidance scenario. With the increasing application of intelligent robots, the complex dynamic environment puts forward higher requirements for their obstacle avoidance ability. Traditional obstacle avoidance algorithms are difficult to adapt to a changing environment. Reinforcement learning shows strong adaptability and obstacle avoidance effects through the interaction between robots and the environment, which has become a current research hotspot. In this paper, based on Q-learning and the Sarsa algorithm, a Python program is used to build the experimental environment, and the test scene is processed graphically to facilitate the observation of the obstacle avoidance path of the robot. Both Q-learning and the State-Action-Reward-State-Action (SARSA) algorithm avoid conventional obstacles and reach the end point by the shortest path in the experiment. In the dangerous obstacle scene, the Q-learning algorithm can still avoid obstacles and find the shortest path, while the Sarsa algorithm selects a longer route. The verification results show that the two algorithms have their advantages and disadvantages, which provides a reference for the selection and optimization of robot obstacle avoidance algorithms and has important practical significance and theoretical value. This study aims to promote the development of robot obstacle avoidance technology and provide a useful reference for research and application in related fields.

A robot movement modeling algorithm with obstacle avoidance using the Q-learning machine learning method is proposed. Q-learning allows for preserving the rewards obtained during modeling by performing optimal actions in each specific state. The Q-table contains information about the state and actions of the robot. Storing the Q-table in the blockchain using IPFS (InterPlanetary File System) technology ensures reliable and decentralized storage of data about the robot's states and actions. Content addressing in IPFS separates the data from its location and retrieves files from multiple sources in a peer-to-peer mode. A computational experiment for the proposed algorithm was conducted using a robot movement simulation environment. In the Gazebo 11 visualization package, it was shown that using the new algorithm, obstacles are avoided faster (by 59.8 %) compared to the previous version of the algorithm.

Tactile pavings (Tenji Blocks) are prone to wear, obstruction, and improper installation, posing significant safety risks for visually impaired pedestrians. This system incorporates a lightweight YOLOv8 (You Only Look Once version 8) model for real-time detection using a fisheye camera to maximize field-of-view coverage, which is highly advantageous for low-altitude UAV navigation in complex urban settings. To enable lightweight deployment, a novel Lightweight Shared Detail Enhanced Oriented Bounding Box (LSDE-OBB) head module is proposed. The design rationale of LSDE-OBB leverages the consistent structural patterns of tactile pavements, enabling parameter sharing within the detection head as an effective optimization strategy without significant accuracy compromise. The feature extraction module is further optimized using StarBlock to reduce computational complexity and model size. Integrated Contextual Anchor Attention (CAA) captures long-range spatial dependencies and refines critical feature representations, achieving an optimal speed–precision balance. The framework demonstrates a 25.13% parameter reduction (2.308 M vs. 3.083 M), 46.29% lower GFLOPs, and achieves 11.97% mAP50:95 on tactile paving datasets, enabling real-time edge deployment. Validated through public/custom datasets and actual UAV flights, the system realizes robust tactile paving detection and stable navigation in complex urban environments via hierarchical control algorithms for dynamic trajectory planning and obstacle avoidance, providing an efficient and scalable platform for automated infrastructure inspection.

Abstract Underwater robots conducting inspections require autonomous obstacle avoidance capabilities to ensure safe operations. Training methods based on reinforcement learning (RL) can effectively develop autonomous obstacle avoidance strategies for underwater robots; however, training in real environments carries significant risks and can easily result in robot damage. This paper proposes a Sim-to-Real pipeline for RL-based training of autonomous obstacle avoidance in underwater robots, addressing the challenges associated with training and deploying RL methods for obstacle avoidance in this context. We establish a simulation model and environment for underwater robot training based on the mathematical model of the robot, comprehensively reducing the gap between simulation and reality in terms of system inputs, modeling, and outputs. Experimental results demonstrate that our high-fidelity simulation system effectively facilitates the training of autonomous obstacle avoidance algorithms, achieving a 94% success rate in obstacle avoidance and collision-free operation exceeding 5000 steps in virtual environments. Directly transferring the trained strategy to a real robot successfully performed obstacle avoidance experiments in a pool, validating the effectiveness of our method for autonomous strategy training and sim-to-real transfer in underwater robots.

In this paper, we propose a hybrid algorithm that integrates an improved artificial potential field method (IAPF), model predictive control (MPC), and an extended state observer (ESO) to address the obstacle avoidance problem in multi-unmanned surface vehicle (Multi-USV) formations, including both dynamic and static obstacles, as well as navigation through narrow waterways. Initially, the virtual structure method was applied for formation control. Next, the traditional potential field method was enhanced by employing a saturated attractive potential field and a partitioned repulsive potential field, which improve formation stability and obstacle avoidance accuracy in complex environments. The extended state observer was then employed to estimate and compensate for unknown system dynamics and external disturbances from the marine environment in real time, improving system robustness. On this basis, by leveraging the multi-step predictive optimization capabilities of model predictive control, the proposed algorithm dynamically adjusts control inputs based on the desired trajectories generated from potential field forces, which ensures the stability of formation control and effective obstacle avoidance. The simulation results demonstrate that the proposed algorithm effectively avoids both dynamic and static obstacles in multi-unmanned surface vehicle formations and enables successful navigation through narrow waterways by altering the formation.

Aiming at the problem of multi-objective path planning (MOPP) and dynamic obstacle avoidance of manipulator in dynamic environment, this paper proposes a solution based on hierarchical reinforcement learning (HRL) framework. Traditional path planning methods have some problems in dynamic scenes, such as poor real-time performance, difficult to balance multi-objective conflicts and insufficient adaptability to environmental changes. Therefore, this paper designs a two-tier architecture including global path planning layer and local obstacle avoidance layer, which is trained by Proximal policy optimization (PPO) and Soft Actor-Critic (SAC) algorithms respectively, and achieves collaborative optimization through an efficient information exchange mechanism between layers. At the same time, a multi-objective reward function based on dynamic weight adjustment strategy is introduced. Fuzzy logic is used to adaptively balance the relationship among path length, obstacle avoidance safety and energy consumption according to environmental complexity. Combined with Long Short-Term Memory (LSTM), the trajectory of obstacles is predicted, and the potential field method is further introduced to modify the obstacle avoidance reward, which improves the real-time response ability and robustness of the algorithm in dynamic environment. The experimental results show that the HRL-SAC-PPO method proposed in this paper shows superior performance in both static and dynamic scenarios. In the static scene, the success rate of this method reaches 100%, the average path length is shortened to 2.13m, no collision occurs, and the energy consumption is reduced to 1.12 kJ, which shows a good multi-objective optimization effect. In the dynamic scene, the trajectory error of obstacles predicted by LSTM is only 4.2%, and the safe distance between the robot arm and obstacles is improved by 35%, which significantly enhances the reliability of obstacle avoidance. In addition, the average decision delay of this method is only 11.3ms, and the peak delay is 23ms, which is much lower than that of the contrast algorithm, showing stronger real-time response ability. The ablation experiment further verified the key role of LSTM trajectory prediction, dynamic weight adjustment and layered structure on the overall performance. In the welding task verification of the real UR5 manipulator, the success rate of the system in dynamic environment is 95.3%, the average path length is 3.41m, and the maximum joint acceleration is only 0.87 radian/s², which is far below the safety threshold, indicating that the algorithm has good stability and obstacle avoidance ability in practical application. The comprehensive performance comparison shows that this method performs well in different industrial scenarios, and has stronger environmental adaptability and comprehensive path planning ability.

This paper presents a collision-based probabilistic Vector Field Histogram (p-VFH) obstacle avoidance algorithm for swarm robot navigation in unknown environments. Conventional obstacle avoidance strategies, including well-known path planning methods like A* and RRT*, are ill-suited for unknown environments. Moreover, current collision avoidance approaches for robot swarms face challenges related to computational demands, sensor performance, and potential local minima issues, such as deadlocks. Our proposed p-VFH algorithm tackles these problems by employing a probabilistic approach to determine the robot's movement trajectory. This method relies on a dynamically updated polar histogram that represents obstacle density in the surrounding area, and target direction. The algorithm begins by initializing histogram values, which are then updated as the robot encounters unrecorded obstacles. Subsequently, it creates a probability distribution to guide the selection of the next movement direction. To assess the effectiveness of p-VFH, we conducted comprehensive simulation studies. These experiments compared p-VFH's performance against three alternative methods including conventional VFH, a combined probability distribution approach, and a constant weighting function. The results show that p-VFH significantly improves exploration efficiency, successfully guiding robots to designated targets while effectively avoiding obstacles. In particular, p-VFH outperformed the other tested methods in terms of success rates and environmental adaptability. Furthermore, we conducted real-world experiments using a swarm robots equipped with the p-VFH algorithm. These real-world tests confirmed the effectiveness of p-VFH in real-time obstacle avoidance and exploration in unknown environments. The promising results suggest that the p-VFH algorithm could play a crucial role in advancing swarm robotics technology, with potential applications ranging from planetary exploration to various other fields.

CoNi-MPC [1] provides an efficient framework for UAV control in air-ground cooperative tasks by relying exclusively on relative states, eliminating the need for global state estimation. However, its lack of environmental information poses significant challenges for obstacle avoidance. To address this issue, we propose a novel obstacle avoidance algorithm, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Co</b>operative <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b>on-<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</b>nertial frame-based <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b>bstacle <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b>voidance (CoNi-OA), designed explicitly for UAV-UGV cooperative scenarios without reliance on global state estimation or obstacle prediction. CoNi-OA uniquely utilizes a single frame of raw LiDAR data from the UAV to generate a modulation matrix, which directly adjusts the quadrotor's velocity to achieve obstacle avoidance. This modulation-based method enables real-time generation of collision-free trajectories within the UGV's non-inertial frame, significantly reducing computational demands (less than 5 ms per iteration) while maintaining safety in dynamic and unpredictable environments. The key contributions of this work include: (1) a modulation-based obstacle avoidance algorithm specifically tailored for UAV-UGV cooperation in non-inertial frames without global states; (2) rapid, real-time trajectory generation based solely on single-frame LiDAR data, removing the need for obstacle modeling or prediction; and (3) adaptability to both static and dynamic environments, thus extending applicability to featureless or unknown scenarios.